Investigation of major genes affecting body weight in hair goats using bayesian segregation analysis

The objective of this study is to investigate the presence of major genes affecting body weight in hair goats. The application of Bayesian segregation analysis to big data facilitates more precise identification of intricate genetic structures and variations. This approach offers more profound biological insights through the detection of concealed genetic elements within big datasets. The precise quantification of additive genetic effects is fundamental for achieving sustainable genetic progress through targeted selection. Furthermore, the evaluation of dominance effects offers critical insights into heterozygote advantage, elucidating the mechanisms underlying heterosis and resilience in growth-related traits within livestock populations.

To rapidly and accurately identify the presence of major genes, pedigree data and phenotypic data were employed in a Bayesian segregation analysis. For this purpose, 4072 records of body weight were analysed, measured at two different time points (birth weight (Time1) and body weight measured at approximately 100-120 days of age (Time2)). The data set comprised 2036 animals (n = 1038 male, n = 998 female). Gibbs sampling was employed to make statistical inferences regarding posterior distributions. These inferences were based on 20 replications of the Markov chain for each trait, with 100,000 samples collected, with each 500th sample retained due to the high correlation among the samples.

In this study, the estimated error variance, major gene variance, polygenic variance, dominance effect, and additive genetic effect were determined through Bayesian segregation analysis. The dominance effect (-1.797) was found to be smaller than the additive genetic effect (3.594) for birth weight, whereas for body weight at 4 months of age, the dominance effect (55.902) was found to be higher than the additive genetic variance (54.988). The polygenic and major gene heritabilities were estimated to be 0.51 (± 0.56) and 0.81 (± 0.91) for body weight, and 0.44 (± 0.55) and 0.86 (± 0.93) for body weight at four months of age, respectively.

The results of this study indicate that the 95% highest posterior density regions (HPDs) for the major gene parameter, particularly for the major gene variance, do not include 0, indicating the statistical significance of the major gene component.